// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <include/ocr_rec.h>

namespace PaddleOCR {

void CRNNRecognizer::Run(
    std::vector<cv::Mat> img_list,
    std::vector<std::string>& rec_texts,
    std::vector<float>& rec_text_scores,
    std::vector<double>& times
)
{
    std::chrono::duration<float> preprocess_diff =
        std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
    std::chrono::duration<float> inference_diff =
        std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
    std::chrono::duration<float> postprocess_diff =
        std::chrono::steady_clock::now() - std::chrono::steady_clock::now();

    int img_num = img_list.size();
    std::vector<float> width_list;
    for (int i = 0; i < img_num; i++) {
        width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
    }
    std::vector<int> indices = Utility::argsort(width_list);

    for (int beg_img_no = 0; beg_img_no < img_num;
         beg_img_no += this->rec_batch_num_) {
        auto preprocess_start = std::chrono::steady_clock::now();
        int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
        int batch_num = end_img_no - beg_img_no;
        int imgH = this->rec_image_shape_[1];
        int imgW = this->rec_image_shape_[2];
        float max_wh_ratio = imgW * 1.0 / imgH;
        for (int ino = beg_img_no; ino < end_img_no; ino++) {
            int h = img_list[indices[ino]].rows;
            int w = img_list[indices[ino]].cols;
            float wh_ratio = w * 1.0 / h;
            max_wh_ratio = max(max_wh_ratio, wh_ratio);
        }

        int batch_width = imgW;
        std::vector<cv::Mat> norm_img_batch;
        for (int ino = beg_img_no; ino < end_img_no; ino++) {
            cv::Mat srcimg;
            img_list[indices[ino]].copyTo(srcimg);
            cv::Mat resize_img;
            this->resize_op_.Run(
                srcimg, resize_img, max_wh_ratio, this->use_tensorrt_,
                this->rec_image_shape_
            );
            this->normalize_op_.Run(
                &resize_img, this->mean_, this->scale_, this->is_scale_
            );
            norm_img_batch.push_back(resize_img);
            batch_width = max(resize_img.cols, batch_width);
        }

        std::vector<float> input(batch_num * 3 * imgH * batch_width, 0.0f);
        this->permute_op_.Run(norm_img_batch, input.data());
        auto preprocess_end = std::chrono::steady_clock::now();
        preprocess_diff += preprocess_end - preprocess_start;
        // Inference.
        auto input_names = this->predictor_->GetInputNames();
        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
        input_t->Reshape({batch_num, 3, imgH, batch_width});
        auto inference_start = std::chrono::steady_clock::now();
        input_t->CopyFromCpu(input.data());
        this->predictor_->Run();

        std::vector<float> predict_batch;
        auto output_names = this->predictor_->GetOutputNames();
        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
        auto predict_shape = output_t->shape();

        int out_num = std::accumulate(
            predict_shape.begin(), predict_shape.end(), 1,
            std::multiplies<int>()
        );
        predict_batch.resize(out_num);

        output_t->CopyToCpu(predict_batch.data());
        auto inference_end = std::chrono::steady_clock::now();
        inference_diff += inference_end - inference_start;
        // ctc decode
        auto postprocess_start = std::chrono::steady_clock::now();
        for (int m = 0; m < predict_shape[0]; m++) {
            std::string str_res;
            int argmax_idx;
            int last_index = 0;
            float score = 0.f;
            int count = 0;
            float max_value = 0.0f;

            for (int n = 0; n < predict_shape[1]; n++) {
                argmax_idx = int(Utility::argmax(
                    &predict_batch
                        [(m * predict_shape[1] + n) * predict_shape[2]],
                    &predict_batch
                        [(m * predict_shape[1] + n + 1) * predict_shape[2]]
                ));
                max_value = float(*std::max_element(
                    &predict_batch
                        [(m * predict_shape[1] + n) * predict_shape[2]],
                    &predict_batch
                        [(m * predict_shape[1] + n + 1) * predict_shape[2]]
                ));

                if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
                    score += max_value;
                    count += 1;
                    str_res += label_list_[argmax_idx];
                }
                last_index = argmax_idx;
            }
            score /= count;
            if (isnan(score)) {
                continue;
            }
            rec_texts[indices[beg_img_no + m]] = str_res;
            rec_text_scores[indices[beg_img_no + m]] = score;
        }
        auto postprocess_end = std::chrono::steady_clock::now();
        postprocess_diff += postprocess_end - postprocess_start;
    }
    times.push_back(double(preprocess_diff.count() * 1000));
    times.push_back(double(inference_diff.count() * 1000));
    times.push_back(double(postprocess_diff.count() * 1000));
}

void CRNNRecognizer::LoadModel(const std::string& model_dir)
{
    //   AnalysisConfig config;
    paddle_infer::Config config;
    config.SetModel(
        "C:\\Users\\HUAWEI\\.paddleocr\\whl\\rec\\en\\en_PP-OCRv4_rec_"
        "infer\\inference.pdmodel",
        "C:\\Users\\HUAWEI\\.paddleocr\\whl\\rec\\en\\en_PP-OCRv4_rec_"
        "infer\\inference.pdiparams"
    );

    // std::cout << "In PP-OCRv3, default rec_img_h is 48,"
    //           << "if you use other model, you should set the param
    //           rec_img_h=32"
    //           << std::endl;
    std::cout << config.prog_file() << std::endl;
    std::cout << config.params_file() << std::endl;

    if (this->use_gpu_) {
        config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
        if (this->use_tensorrt_) {
            auto precision = paddle_infer::Config::Precision::kFloat32;
            if (this->precision_ == "fp16") {
                precision = paddle_infer::Config::Precision::kHalf;
            }
            if (this->precision_ == "int8") {
                precision = paddle_infer::Config::Precision::kInt8;
            }
            config.EnableTensorRtEngine(
                1 << 20, 10, 3, precision, false, false
            );
            int imgH = this->rec_image_shape_[1];
            int imgW = this->rec_image_shape_[2];
            std::map<std::string, std::vector<int>> min_input_shape = {
                {"x",            {1, 3, imgH, 10}},
                {"lstm_0.tmp_0", {10, 1, 96}     }
            };
            std::map<std::string, std::vector<int>> max_input_shape = {
                {"x",            {1, 3, imgH, 2000}},
                {"lstm_0.tmp_0", {1000, 1, 96}     }
            };
            std::map<std::string, std::vector<int>> opt_input_shape = {
                {"x",            {1, 3, imgH, imgW}},
                {"lstm_0.tmp_0", {25, 1, 96}       }
            };

            config.SetTRTDynamicShapeInfo(
                min_input_shape, max_input_shape, opt_input_shape
            );
        }
    } else {
        config.DisableGpu();
        if (this->use_mkldnn_) {
            config.EnableMKLDNN();
            // cache 10 different shapes for mkldnn to avoid memory leak
            config.SetMkldnnCacheCapacity(10);
        }
        config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
    }

    // get pass_builder object
    auto pass_builder = config.pass_builder();
    // delete "matmul_transpose_reshape_fuse_pass"
    pass_builder->DeletePass("matmul_transpose_reshape_fuse_pass");
    config.SwitchUseFeedFetchOps(false);
    // true for multiple input
    config.SwitchSpecifyInputNames(true);

    config.SwitchIrOptim(true);

    config.EnableMemoryOptim();
    //   config.DisableGlogInfo();

    this->predictor_ = CreatePredictor(config);
}

} // namespace PaddleOCR
